Biomedical subjects

Radhakrishnan Nagarajan

Publications and source records attributed to Radhakrishnan Nagarajan.

3 recordsLinked to original sources

PubMed2003

Intensity-based segmentation of microarray images.

The underlying principle in microarray image analysis is that the spot intensity is a measure of the gene expression. This implicitly assumes the gene expression of a spot to be governed entirely by the distribution of the pixel intensities. Thus, a segmentation technique based on the distribution of the pixel intensities is appropriate for the current problem. In this paper, clustering-based segmentation is described to extract the target intensity of the spots. The approximate boundaries of the spots in the microarray are determined by manual adjustment of rectilinear grids. The distribution of the pixel intensity in a grid containing a spot is assumed to be the superposition of the foreground and the local background. The k-means clustering technique and the partitioning around medoids (PAM) were used to generate a binary partition of the pixel intensity distribution. The median (k-means) and the medoid (PAM) of the cluster members are chosen as the cluster representatives. The effectiveness of the clustering-based segmentation techniques was tested on publicly available arrays generated in a lipid metabolism experiment (Callow et al., 2000). The results are compared against those obtained using the region-growing approach (SPOT) (Yang et al., 2001). The effect of additive white Gaussian noise is also investigated.

Algorithms↗

PubMed2002

Microarray analysis of gene expression during early adipocyte differentiation.

The molecular mechanisms that regulate cellular differentiation during development and throughout life are complex. It is now recognized that precise patterns of differentially expressed genes ultimately direct a particular cell toward a given lineage and many of these are regulated during the earliest stages of differentiation. Using a microarray-based expression analysis, we have examined gene expression profiles during the first 24 h of 3T3-L1 adipocyte differentiation. RNA was isolated at times 0, 2, 8, 16, and 24 h following stimulation of differentiation and hybridized in duplicate to high density Affymetrix microarray gene chips containing a series of 13,179 cDNA/expressed sequence tag (EST) probe sets. Two hundred and eighty-five cDNA/ESTs were shown to have at least a fivefold change in expression levels during this time course and both hierarchical and self-organizing map clustering analysis was performed to categorize them by expression profiles. Several genes known to be regulated during this time period were confirmed and Western blot analysis of the proteins encoded by some of the identified genes revealed expression profiles similar to their mRNA counterparts. As expected, many of the genes identified have not been examined in such a critical time period during adipogenesis and may well represent novel adipogenic mediators.

3T3 Cells↗

PubMed2002

Quantifying physiological data with Lempel-Ziv complexity--certain issues.

The oscillations observed in physiological data can be attributed largely to the presence of either a nonlinear deterministic or a nondeterministic component. The Lempel-Ziv complexity and its variants have been used successfully to quantify the regularity of these oscillations. The decrease in the complexity can be observed in the case of nontrivial deterministic patterns as well as correlated noise. Thus, any conclusion on the nature of the pattern based solely on the value of the Lempel-Ziv complexity is incomplete. In this paper, the use of the surrogate data technique is suggested to avoid spurious interpretation of this measure of complexity. The data sets considered include the uterine contraction obtained during active labor. The surrogates are generated using the amplitude adjusted fourier transform and the iterated amplitude adjusted fourier transform. The approximate entropy is used as an alternate measure to verify the results obtained.

Cluster Analysis↗